With the proliferation of online platforms

an increasing number of people are immersing themselves in the virtual world

leading to reduced real-world social interactions

more introverted personalities

and even psychological conditions such as depression. Therefore

the implementation of facial emotion recognition technology is significant for early detection of negative emotions and providing timely humanistic care. Emotion recognition not only helps individuals better understand their own emotions but also promotes positive self-perception and mental health. By analyzing facial expressions

one can effectively determine a person's emotional state. Research on facial expression recognition began in the 1970s

and with the development of image processing and pattern recognition technologies

it has gradually transitioned from traditional methods to automated recognition based on deep learning. This paper reviews recent research achievements in the field of emotion recognition

focusing on how to improve the accuracy and efficiency of emotion recognition to promote its widespread application in real life. By analyzing three representative studies

this paper summarizes the current shortcomings of emotion recognition technology

including insufficient robustness

real-time issues

limitations of datasets

inadequate model complexity and optimization

and insufficient multi-modal fusion. In response to these shortcomings

this paper proposes corresponding directions for improvement

such as enhancing environmental adaptability

optimizing model structure

expanding dataset scale

improving loss functions

and adopting multi-modal fusion. These directions aim to improve the adaptability

real-time performance

and accuracy of emotion recognition technology in complex environments

while enhancing its generalization ability and practicality. These research progress and suggestions for improvement provide valuable references for the future development of emotion recognition technology.